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All Journal Bulletin of Electrical Engineering and Informatics Journal of Soft Computing Exploration
Fajar Muslim
Faculty Of Information Technology And Data Science, Universitas Sebelas Maret, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Design and evaluation of a secure key exchange protocol using the Kyber-Dilithium algorithm Bambang Harjito; Muhammad Defaroyan; Fajar Muslim; Ery Permana Yudha; Endra Pratama
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11157

Abstract

Over 90% of the billions of people who use the internet globally use it through the transport layer security (TLS) protocol. TLS is a security standard that performs network authentication and data encryption when accessing the internet. Authenticated key exchange (AKE) is the protocol TLS uses for network authentication and key establishment during the TLS Handshake process. The AKE protocol utilizes a public key cryptosystem (PKC) and digital signatures with algorithms commonly used, namely elliptic curve cryptography (ECC) and Rivest-Shamir-Adleman (RSA). Future advancements in quantum computing may compromise the security of the widely used ECC and RSA algorithms. This research conducts an implementation and comparative analysis of post-quantum algorithms resistant to quantum computer attacks, specifically Kyber-Dilithium, in the context of the AKE protocol. The implementation is performed at three security levels: 128-bit, 192-bit, and 256-bit. The results show that the Kyber-Dilithium is greater than those of the RSA variant and much larger than those of the ECC variant. In contrast to the ECC and RSA variants, the Kyber-Dilithium algorithm variants perform better across all security levels, even if their byte sizes are greater.
Clickbait detection in indonesian news headlines using various prompting strategies in large language models Nandhika Rega Rohadi; Fajar Muslim; Dewi Wisnu Wardani
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.131

Abstract

Clickbait detection in news headlines is a critical task in Natural Language Processing (NLP) related to information quality and the credibility of digital journalism. While traditional machine learning and deep learning approaches have demonstrated impressive performance in clickbait detection, they are limited by a heavy reliance on extensive annotated datasets and significant computational requirements for model training. Unlike previous methods, Large Language Models (LLMs) do not require massive amounts of annotated data. LLMs allow classification tasks to be solved through zero-shot and few-shot prompting without additional retraining. However, the effectiveness of these models can depend significantly on prompting configuration. Despite this, linguistically-enriched prompting strategies have not been widely evaluated for non-English domains such as Indonesian news headlines. This study aims to analyze the performance of various LLM prompting strategies in detecting Indonesian-language clickbait headlines. For this purpose, this study evaluated Llama 4 on the CLICK-ID dataset using multiple combinations of plain and linguistically-enriched prompts (zero-shot and few-shot) alongside advanced inference techniques (self-consistency, weighted self-consistency, and Self-Refine). Performance was measured via Accuracy and Macro F1-scores against a fine-tuned IndoBERT as baseline model. The results show that the prompting approach in the Large Language Models (LLMs) can be used to classify Indonesian clickbait effectively. The use of linguistic prompting and few-shot managed to provide the best performance, which achieved an accuracy of 0.90 and a Macro F1-score of 0.89. 
Co-Authors Bambang Harjito, Bambang Dewi Wisnu Wardani Endra Pratama Ery Permana Yudha Muhammad Defaroyan Nandhika Rega Rohadi